After obtaining the warped source image feature, we need to use this information to generate the final output. Figure REF  shows the architecture of texture renderer. In order to better preserve the information of texture feature, we have borrowed from the SPADE network structure [1]} . The texture features of different sizes are used as the input of the SPADE module. But we need to emphasize that: (1) As opposed to [1]}, we replaced the BN layer in the SPADE module with the IN layer, because in the image generation task, different images have different styles, therefore, the IN layer that performs feature statistics on a single channel of a single instance is more suitable. (2) We did not use constant code as the input of texture renderer, but target pose features. Compared to the constant code, the target pose feature has richer information, which can speed up the convergence of the model. What's more, target pose features can provide position constraints for texture features, making the dense corresponding more accurate, leading to more realistic generated image.
